Individual Participant Data Meta-Analysis: A Handbook for
Healthcare Research provides a comprehensive introduction to the
fundamental principles and methods that healthcare researchers need
when considering, conducting or using individual participant data
(IPD) meta-analysis projects. Written and edited by researchers
with substantial experience in the field, the book details key
concepts and practical guidance for each stage of an IPD
meta-analysis project, alongside illustrated examples and summary
learning points. Split into five parts, the book chapters take the
reader through the journey from initiating and planning IPD
projects to obtaining, checking, and meta-analysing IPD, and
appraising and reporting findings. The book initially focuses on
the synthesis of IPD from randomised trials to evaluate treatment
effects, including the evaluation of participant-level effect
modifiers (treatment-covariate interactions). Detailed extension is
then made to specialist topics such as diagnostic test accuracy,
prognostic factors, risk prediction models, and advanced
statistical topics such as multivariate and network meta-analysis,
power calculations, and missing data. Intended for a broad
audience, the book will enable the reader to: Understand the
advantages of the IPD approach and decide when it is needed over a
conventional systematic review Recognise the scope, resources and
challenges of IPD meta-analysis projects Appreciate the importance
of a multi-disciplinary project team and close collaboration with
the original study investigators Understand how to obtain, check,
manage and harmonise IPD from multiple studies Examine risk of bias
(quality) of IPD and minimise potential biases throughout the
project Understand fundamental statistical methods for IPD
meta-analysis, including two-stage and one-stage approaches (and
their differences), and statistical software to implement them
Clearly report and disseminate IPD meta-analyses to inform policy,
practice and future research Critically appraise existing IPD
meta-analysis projects Address specialist topics such as effect
modification, multiple correlated outcomes, multiple treatment
comparisons, non-linear relationships, test accuracy at multiple
thresholds, multiple imputation, and developing and validating
clinical prediction models Detailed examples and case studies are
provided throughout.
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